PDF电子书：Yeast Systems Biology

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Yeast Systems Biology
; `6 T5 c/ \. Z' aMethods and Protocols, s7 ]* [# Z/ L; N1 K/ M; v1 l5 Q
Edited by5 b8 w% L% u( B4 k' R5 d
Juan I. Castrillo2 S4 @' ]) U% e5 n* `8 q; I
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Contents+ b$ N# `! N% J7 ]" G
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
. q% T; ~3 A& Y  r5 h. P7 [Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi2 `% u" ^0 s8 s- J% [
SECTION I: YEAST SYSTEMS BIOLOGY
: L# y5 H( {: A6 _1. Yeast Systems Biology: The Challenge of Eukaryotic Complexity . . . . . . . . . 3, ~! b2 W& q; x! G- E# o( C( N
Juan I. Castrillo and Stephen G. Oliver
. I' i" t+ e% ^) n- tSECTION II: EXPERIMENTAL SYSTEMS BIOLOGY: HIGH-THROUGHPUT GENOME-WIDE# E- ?* z5 |7 R  h: |' B3 [$ N
AND MOLECULAR STUDIES
+ [$ ]1 m, k( q  ]1 k2. Saccharomyces cerevisiae: Gene Annotation and Genome Variability, State
! L9 [4 I) |) ^  h, L2 z. nof the Art Through Comparative Genomics . . . . . . . . . . . . . . . . . . . . 31# ]& `- ~3 v; I+ T
Ed Louis
1 F' L% Q' a( [0 V, f9 X2 ]( R3. Genome-Wide Measurement of Histone H3 Replacement Dynamics in Yeast . . 41
6 N0 O* v$ C- V2 ]# _+ zOliver J. Rando
7 e3 m# D6 s: y& _) A( q; [, [4. Genome-Wide Approaches to Studying Yeast Chromatin Modifications . . . . . 61
$ Q$ a% _- k& }  I' UDustin E. Schones, Kairong Cui, and Suresh Cuddapah
6 i9 E9 J9 U) }0 h5. Absolute and Relative Quantification of mRNA Expression (Transcript Analysis) . 73
4 c! ?7 U+ g  ^2 u2 {Andrew Hayes, Bharat M. Rash, and Leo A.H. Zeef
# X. A5 g$ W9 @) i# L" B6. Enrichment of Unstable Non-coding RNAs and Their Genome-Wide
* `; r. u! i# f' p# b7 g# fIdentification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
3 X/ T% n/ `: l2 C! g8 gHelen Neil and Alain Jacquier
( r: F$ ?; n$ O* B; j$ m7. Genome-Wide Transcriptome Analysis in Yeast Using High-Density
3 h% g& }5 s/ S  QTiling Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
0 O' C$ ^9 h5 X! R+ ^* XLior David, Sandra Clauder-Münster, and Lars M. Steinmetz$ ?4 ?" }/ ~4 `  @+ T4 y. Q( G
8. RNA Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125+ t( Q- P6 A) _/ O- C8 G3 {
Karl Waern, Ugrappa Nagalakshmi, and Michael Snyder
! s  E$ B8 G. [; x9. Polyadenylation State Microarray (PASTA) Analysis . . . . . . . . . . . . . . . 133
/ I- g- q6 G) h& v6 jTraude H. Beilharz and Thomas Preiss
8 ~" f8 w. N5 G5 X& e5 \' _9 V; n3 B10. Enabling Technologies for Yeast Proteome Analysis . . . . . . . . . . . . . . . . 149' R0 u6 V4 _2 q. ]- H% ?
Johanna Rees and Kathryn Lilley7 H& @2 @- v, g9 P$ v( u7 \, n
11. Protein Turnover Methods in Single-Celled Organisms: Dynamic SILAC . . . . 1791 m. J# a9 `4 P) f* P1 R3 J: a
Amy J. Claydon and Robert J. Beynon/ l% A- l" Z3 Z
12. Protein–Protein Interactions and Networks: Forward and Reverse Edgetics . . . 197
2 ^  q2 z+ c& Z: P& G. SBenoit Charloteaux, Quan Zhong, Matija Dreze, Michael E. Cusick," Z* r4 q) p$ A, g8 A% W2 y8 j
David E. Hill, and Marc Vidal
/ y" q' i% Y2 o! n! q2 w) i13. Use of Proteome Arrays to Globally Identify Substrates for E3
3 {$ C# ?/ `! D0 Y1 b; ^+ zUbiquitin Ligases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
: g' a9 `1 U+ N9 c2 I- u- [0 ]7 ?Avinash Persaud and Daniela Rotin
/ u/ U. z; P6 k% V; n/ d14. Fit-for-Purpose Quenching and Extraction Protocols for Metabolic
2 j8 ^- U6 a( `9 B$ {: KProfiling of Yeast Using Chromatography-Mass Spectrometry Platforms . . . . . 225$ A  O5 i! C# O
Catherine L. Winder and Warwick B. Dunn; l4 Q! G6 B" G, K* y3 o
15. The Automated Cell: Compound and Environment Screening System3 ?" b- ]+ x8 r% P
(ACCESS) for Chemogenomic Screening . . . . . . . . . . . . . . . . . . . . . 239+ |8 ?+ J0 k/ z5 m0 I' o
Michael Proctor, Malene L. Urbanus, Eula L. Fung,
9 Y  o8 n1 J! R1 y  ]# e- ]5 HDaniel F. Jaramillo, Ronald W. Davis, Corey Nislow,7 p" _$ J- o# x, V7 Z( |& E
and Guri Giaever# x3 S5 `; _% ~, C1 D
16. Competition Experiments Coupled with High-Throughput Analyses for' K2 S8 Q8 o  v3 O
Functional Genomics Studies in Yeast . . . . . . . . . . . . . . . . . . . . . . . 271
* I, K4 P! f! U. q4 i! [/ N9 l. wDaniela Delneri1 S& \+ |% _+ V( D
17. Fluorescence Fluctuation Spectroscopy and Imaging Methods for: Q+ @( e0 y- t% [
Examination of Dynamic Protein Interactions in Yeast . . . . . . . . . . . . . . 283
+ S: D6 P( `0 z) e8 T* i* uBrian D. Slaughter, Jay R. Unruh, and Rong Li
5 Z0 S' @% E) x9 R0 l2 j3 \18. Nutritional Control of Cell Growth via TOR Signaling in Budding Yeast . . . . . 307. T  [5 `3 \1 j" |) ]) w
Yuehua Wei and X.F. Steven Zheng% O  ?# ]& W  X* m4 L' Y
SECTION III: COMPUTATIONAL SYSTEMS BIOLOGY: COMPUTATIONAL STUDIES& U( G# S9 o' ^  Y% R' Y$ F
AND ANALYSES
8 l) [% P3 x. ?9 \* u19. Computational Yeast Systems Biology: A Case Study for the MAP
- @* ^1 C" l0 C7 ?$ [. z- d  cKinase Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323) e+ p, J, Q/ s$ i
Edda Klipp9 {4 d# k) e% _' T: r
20. Standards, Tools, and Databases for the Analysis of Yeast ‘Omics Data . . . . . . 345
) |+ G( O8 L- c0 n; I# xAxel Kowald and Christoph Wierling3 u( m  H* i% G' S. J. n! o. N. W
21. A Computational Method to Search for DNA Structural Motifs in
3 T9 y4 z" q+ \+ `" V# v% Z3 AFunctional Genomic Elements . . . . . . . . . . . . . . . . . . . . . . . . . . 3677 X  ?$ s3 m8 Q4 X$ s. J
Stephen C.J. Parker, Aaron Harlap, and Thomas D. Tullius) c. Z- v$ k# U" B# ~4 Q. z. N% l
22. High-Throughput Analyses and Curation of Protein Interactions in Yeast . . . . 381
: ^/ }. W6 @4 O9 B: \8 |; {& OShoshana J. Wodak, Jim Vlasblom, and Shuye Pu
! F: V2 q* S, G0 o5 F2 Z( u5 }8 i+ S23. Noise in Biological Systems: Pros, Cons, and Mechanisms of Control . . . . . . 407# P! F/ ]! y! f" r
Yitzhak Pilpel
. V* J5 H9 P" o; D$ F24. Genome-Scale Integrative Data Analysis and Modeling of Dynamic
$ |/ h) ]6 U: kProcesses in Yeast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427
' M. ?0 l) B6 u4 lJean-Marc Schwartz and Claire Gaugain) o6 z# `2 [8 @% b
25. Genome-Scale Metabolic Models of Saccharomyces cerevisiae . . . . . . . . . . . 445. l) k& I" z8 F7 D
Intawat Nookaew, Roberto Olivares-Hernández, Sakarindr
# T% T; t( F' I, H1 wBhumiratana, and Jens Nielsen
! x* T  L/ d0 u/ o% d26. Representation, Simulation, and Hypothesis Generation in Graph
& K1 R7 V* j0 C. D* eand Logical Models of Biological Networks . . . . . . . . . . . . . . . . . . . . 465+ _3 a' ~2 D3 J, C& z$ r% R8 N9 {2 B
Ken Whelan, Oliver Ray, and Ross D. King0 q  O3 `& ^9 f3 K
27. Use of Genome-Scale Metabolic Models in Evolutionary Systems Biology . . . . 4830 Y/ ^" k9 r+ |
Balázs Papp, Balázs Szappanos, and Richard A. Notebaart
! E& y6 W  ^) X; e/ j+ r2 aSECTION IV: YEAST SYSTEMS BIOLOGY IN PRACTICE: SACCHAROMYCES CEREVISIAE
  F- g1 C! C; y# _- C9 T5 ^2 JAS A TOOL FOR MAMMALIAN STUDIES
. J3 }( W5 z  \  N28. Contributions of Saccharomyces cerevisiae to Understanding Mammalian( m4 @6 d4 P, \- C
Gene Function and Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501
# F, o& K' c+ u2 ONianshu Zhang and Elizabeth Bilsland4 B! q8 z! J$ b! x# Z0 \
Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525
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